Ementorhub

What you'll learn

Describe the advantages and disadvantages of trees, and how and when to use them.

Apply SVMs for binary classification or K > 2 classes.

Analyze the strengths and weaknesses of neural networks compared to other machine learning algorithms, such as SVMs.

Skills you'll gain

Random Forest Algorithm

Supervised Learning

Statistical Machine Learning

Classification And Regression Tree (CART)

Dimensionality Reduction

Applied Machine Learning

Machine Learning Algorithms

Decision Tree Learning

Artificial Intelligence and Machine Learning (AI/ML)

Deep Learning

Machine Learning

Predictive Modeling

Unsupervised Learning

Feature Engineering

Data Science

Artificial Neural Networks

There are 4 modules in this course

This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

Support Vector Machines (SVMs)

Introduction to Neural Networks

Decision Trees-Bagging-Random Forests